Dear Colleagues,

Nature-inspired optimization algorithms represent a very important research field in computational intelligence, soft computing, and optimization in a general sense. For this purpose, we observe clearly that they attract outstanding interest from many researchers around the world. Indeed, past and ongoing research in this field cover an important group of subjects, from basic research to a large number of real-world applications in almost all areas, which include science, engineering, industry, economics, and business. The creation of many new algorithms based on natural processes like natural selection, food foraging, physical laws, group movements and other natural models have made this field of research very rich. These algorithms offer very powerful tools to handle these problems, which cannot be solved using traditional and classical mathematical methods, because they not require any mathematical conditions to be satisfied. It should be noted that a general look leads to the finding that nature-inspired algorithms can be generally classified into two main categories: Evolutionary algorithms and swarm intelligence. There are a few algorithms however that do not fall in any of these categories, e.g., gravitational search, harmony search, etc.

The principal aim of this Special Issue is to assemble state-of-the-art contributions on the latest research and development, up-to-date issues, and challenges in the field of nature-inspired optimization algorithms. Proposed submissions should be original, unpublished, and should present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view. Topics of interest include, but are not only limited to:

Swarm Intelligence (SI)-based algorithms:
Ant Colony Optimization,
Ant Lion Optimization,
Artificial Bee Colony,
Bacterial foraging,
Bacterial-GA Foraging,
Bat Algorithm,
BeeHive,
Bumblebees,
Cat swarm,
Consultant-guided search
Cuckoo Search,
Krill Herd,
Monkey search,
Particle Swarm Optimisation,
Weightless Swarm Algorithm.

Bio-inspired (not SI-based) algorithms:
Atmosphere clouds model,
Biogeography based Optimization,
Brain Storm Optimization,
Differential Evolution,
Dolphin echolocation,
Japanese tree frogs calling,
Eco-inspired evolutionary algorithm,
Egyptian Vulture,
Fish-school Search,
Flower pollination Algorithm,
Firefly Algorithms,
Gene expression.

Physics and Chemistry based algorithms:
Big bang-big Crunch,
Black hole,
Central force optimization,
Charged system search,
Electro-magnetism optimization,
Galaxy-based search algorithm,
Gravitational search,
Harmony Search,
Intelligent water drop,
River formation dynamics,
Self-propelled particles,
Simulated Annealing,
Stochastic diffusion search,
Spiral optimization,
Water cycle algorithm.

Dr. Xiao-Zhi Gao
Dr. Allouani Fouad
Guest Editors

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